A Method to Detect Remote Alarm Based on Edge Computing in Wind Energy

Li-fang GAO, Jing WANG, Di LIU, Ze-san LIU, Yang-yang LIAN, Qi-meng LI, Ting-shun LI

Abstract


Wind parks are usually located in places where few people live and is far from control center. Now, wind turbines often are embedded in so many sensors that operators can obtain their performance. But there are some important alarms difficultly to be monitored directly by sensors, such as blade icing which wind turbines in cold regions often encounter. These alarms involve many problems, such as energy losses, mechanical drawbacks and security. These complex alarms usually attribute to combination of many factors such as low temperature, wind speed, blade accelerator. Many scientists and researchers have proposed different methods or created new sensors to detect complex alarms. But these methods or facilities have different drawbacks that can cause low prediction accuracy, even high failure or error rate etc. This paper, taking blade icing for example, provides a practical method to detect remote alarm using monitoring data of wind turbine by machine learning algorithm without any new equipment. This method consists of four processes: preprocessing monitoring data, main features extraction, remote warning model construction, and deploying every sub-model as edge computing node. Firstly, getting massive historic monitoring data of wind turbine in wind park, and classifying; secondly, extracting main trait factors associating with blade icing; and then constructing detection model for each type wind turbine separately via machine learning according to massive historic monitoring data; finally, building edge computing nodes for every wind turbine to predict blade icing with model. This method has many advantages, such as low cost, high accuracy, clearly targeting, and early icing warning. Furtherly, with the help of edge computing, the generalization of machine learning model is improved. This study was completed with practical monitoring data of wind turbines in some park. Results show that the way to detect blade icing via machine learning is feasible and accuracy.

Keywords


Blade icing detection, Machine learning, Edge computing, Time delay


DOI
10.12783/dtetr/acaai2020/34206

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